The Proteomics & Modeling Core, which will be located at the Institute of Systems Biology, will add key levels of biological information, global analysis, and data visualization capabilities to our collaborative investigations of the hepatitis C virushost system. In collaboration with the Microarray & Virology Core, the Proteomics component of this core will generate global profiles of protein levels and protein-protein interactions. Our major goals are the following: Specific Aim 1: To profile global protein expression in HCV-replicon cell lines, cultured fetal hepatocytes transfected with HCV RNA, and standard hepatocyte cell lines using a novel annotated peptide database (APD) technology under development at the ISB. Using an established hepatocyte-specific APD we ultimately will profile changes in global protein profiles prior to and after HCV reinfection of transplanted livers. Specific Aim 2: To profile protein-protein interactions in the host-virus interaction network. We will probe these predictions directly using immunopurification experiments that use HCV proteins, interferon-regulated proteins, and proteins identified by on-going APD building as target proteins. We will identify and quantify interacting components using the novel IDEnT labeling method and mass spectrometry developed at the ISB. We will resolve changes in affinity among interacting components using sequential MgCl, elutions. Specific Aim 3: To computationally select targets for protein-interaction profiling and analyze protein-protein interaction data to produce a host-virus interaction network scaffold. The ISB computational effort will receive processed microarray data fiom the Bioinformatics & Biostatistics core. Starting with a network based on currently available data, we will iteratively select new targets for protein-interaction profiling, evaluate the new network, and select additional targets. Specific Aim 4: To develop and visualize computational models that integrate mRNA and protein expression data with the host-virus network scaffold to generate predictive models of host-virus network states. Finally we intend to compute a modular model of the network and identify active subnetworks within this modular map.